The Pre-Entrepreneurial Development
of Technological Entrepreneurs
Edward B. Roberts
November 1988
Working Paper # 2091-88
THE PRE-ENTREPRENEURIAL DEVELOPMENT
OF TECHNOLOGICAL ENTREPRENEURS
ABSTRACT
Empirical analyses carried out as part of a twenty years research
program on technology-based enterprises add to our knowledge of the
background characteristics of technological entrepreneurs, specifically
as compared with control groups of employed scientists and engineers.
Perhaps the most important finding is existence of "the entrepreneurial
heritage", a strong tendency for entrepreneurs to come from families in
which the father was self-employed. For those not from
entrepreneurial homes, religious differences affect the incidence of
technical entrepreneurs, but careful analyses dispel the myth of the
first-born son. The typical technical entrepreneur is well-educated, in
his mid-30s, with 13 years of pre-entrepreneurial work experience
during which the prospective entrepreneurs significantly outproduced
their technical colleagues, primarily in the developmental (not
research) end of the R&D work spectrum.
3
The formation and growth of myriad new technology-based firms has
uniquely characterized the United States post-war economy. Indeed,
worldwide interests in replicating patterns in their own countries similar to
U.S. technological entrepreneurship have rapidly increased during the 1980s,
even as U.S. international competitive effectiveness has waned. However,
during these several decades the opportunities presented by advancing
technology have not been seized by all American scientists and engineers.
Very few of the millions of individuals highly trained in existing technology
have taken those steps needed to organize and launch a technological
enterprise. This article seeks better understanding of the backgrounds of
those few who become technological entrepreneurs.
Until recently the creators of new enterprises have been treated in the
literature only in the folkloric tradition of Horatio Alger. Extensive accounts
of the lives of men like J.P. Morgan, Andrew Carnegie, and the Rothchilds
produce a feeling for the spirit and mystique of these capitalist giants.
However, with a handful of exceptions, modern entrepreneurship has not been
subjected to careful empirical examination. Three pioneering
empirically-based works by David McClelland (1961), Everett Hagen(1963),
and Collins & Moore (1964) provide a foundation of theory and perspectives
for more recent explorations. McClelland, primarily a social psychologist,
ties the entrepreneur to the elements of economic change and growth, his
writings being strongly oriented to those psychological characteristics of
entrepreneurs that make them likely to become business innovators. Hagen is
an economist with a sociological bent, interested in explaining economic
growth by the presence in societies of what he calls innovational
personalities. With a strongly empirical psychological orientation Collins and
Moore discuss the origins and experience of entrepreneurs.
None of these early works examined technological entrepreneurs.
Fortunately, increasing numbers of relatively recent studies are examining
the personal backgrounds of technological entrepreneurs, the nature of the
organizations that incubate them, and the processes associated with their
success. (Roberts, 1968; Cooper, 1971; Cooper & Bruno, 1977; Tyebjee &
Bruno, 1982; Van de Ven, Hudson & Schroeder, 1984; Sexton & Smilor, 1986;
Utterback et al., 1988) This article reviews the theories arising from the
foundation literature on the background and development of entrepreneurs in
general and goes beyond the more recent studies in providing new empirical
evidence on the founders of advanced technology enterprises. Our variables of
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emphasis are family background, education, age and work experience;
empirical findings from the recent literature in regard to these dimensions
are included where appropriate throughout the article.
EARLY EVIDENCE ON ENTREPRENEURIAL CHARACTERISTICS
McClelland (1961) sees the entrepreneur as the one who translates need
for achievement (n-ach) into economic development. The entrepreneur in
McClelland's scheme is "the man who organizes the firm (the business unit)
and/or increases its productive capacity." (p. 205) McClelland's underlying
assumption is that entrepreneurs have a high n-ach which will lead them to
behave in certain ways in business situations. McClelland claims that
entrepreneurs thrive on situations in which they can get a sense of personal
achievement through taking responsibility for success and failure.
Entrepreneurs according to McClelland tend to work hard and to do things in an
innovative rather than traditional manner, especially when there is a
challenge and when the completion of work to be done requires ingenuity
rather than standard procedures. But they require concrete feedback in the
form, for example, of production volume or profit as measures of how well or
how poorly they are doing. McClelland argues that among the strongest
factors directly associated with the development of n-ach are parental
values.
Beyond his psychological studies using the Thematic Apperception Test
primarily, McClelland's empirical data are restricted to family backgrounds
and religious variables. He demonstrates differences in n-ach among the three
primary religions in the United States, leading him to conclude (pp. 361, 365):
(1)
More traditional Catholics appear to have some of the values and
attitudes that would be associated with lower need for
achievement.
(2)
Other groups of Catholics exist, at least in the United States and
Germany, which have moved away from some of these traditional
values toward the "achievement ethic" [often associated with
Protestantism].
(3)
There is little doubt that the average need for achievement among
Jews is higher than for the general population in the United States
at the present time.
Everett Hagen, in his book On the Theory of Social Change, defines
entrepreneurship as "... the organization of a group of human beings into a
going concern that carries out a new concept." (p. 87) He, like McClelland,
attempts to explain economic growth by the characteristics of
entrepreneurial groups of people. However, he considers much more than
n-ach as integral to economic development. In his study of economic growth
in Colombia Hagen found that one group of people, the Antioquenos, were more
frequently than any others the founders of substantial enterprises. To a high
degree the Antioquenos manifested needs for autonomy, order and
achievement. Hagen suggests that these personal characteristics increase
the likelihood that the individual will be an innovator which in turn increases
the likelihood that he will be successful as an entrepreneur.
One of the few modern empirical investigations of "the makings" of U.S.
entrepreneurs is the Collins & Moore study, The Enterprising Man. (1964)
In-depth interviews and Thematic Apperception Tests were used to determine
the backgrounds and the psychological motivations behind entrepreneurs'
behavior. Unfortunately from our interest, the 150 business initiators studied
were seldom involved in technology-based companies, most being shopkeepers
or operators of small service businesses. Collins & Moore found that
entrepreneurs tend to subscribe to the Protestant Ethic, (Weber, 1956) a
value system that stresses hard work and striving to produce an earthly, i.e.,
pre-heavenly, reward. According to Collins & Moore, they have an obsessive
drive to push themselves even harder, what we often call "workaholics".
Authority is a difficult area for the entrepreneur. He is unwilling to submit
to it, unable to work with it and has a strong need to escape it. The
entrepreneur cannot easily accept another's leadership and cannot exist in a
situation where his behavior is controlled and dictated by others.
OVERALL SAMPLE SELECTION AND DATA COLLECTION
Beyond these three classic studies of entrepreneurs in general, the
relatively few prior empirical studies of technical entrepreneurship also
referenced above raise three methodological issues that are treated in the
research reported here. All three issues limit the extent to which the
conclusions drawn previously can be deemed reliable. (1) Sample size in
several of those studies are small, leaving questions as to generalizability of
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findings. (2) The samples are often developed from surviving or "successful"
firms, providing no basis for knowing whether the non-survivors are similar
or different in character. For example, use of Dun and Bradstreet listings of
small companies in compiling entrepreneurial samples necessarily omits the
large number (in my experience) of startup firms that never reach the stage
of such listing. (3) Of most importance, none of the prior studies used any
form of control group for comparison with the entrepreneurs. The prior
research does not reveal whether determined characteristics of technical
entrepreneurs are the same or different from those of matched groups of
non-entrepreneurial engineers and scientists.
The data presented here are part of a twenty years research program on
all aspects of the formation and growth of high-technology new enterprises
in the Greater Boston area, including more than 40 separate but related
research studies. Elements of the data collected in sixteen of those studies
(shown in Table 1) are used in this article, covering information from several
hundred firms founded by former employees of the MIT major laboratories and
engineering departments as well as by the former employees of a government
laboratory, a major non-profit systems engineering organization and two
large technological corporations. The four non-MIT organizations were
selected from the Greater Boston area for ease of data collection, seeking
comparability with the size and nature of work of the MIT "sources".
Contrasting information is used from a study of new non-technical
consumer-oriented manufacturing firms. Occasional reference is also made
to findings from other studies within the overall research program.
Wherever possible, these data on the entrepreneurs are compared with
information collected from a control group of scientists and engineers still
employed at two of the largest entrepreneurial "source organizations" that
generated the research samples. In each of these "controls" twenty percent
random samples of employees were created for mail questionnaire data
collection. The high response rate of 76 percent provides some assurance of
representativeness of the control group information, but no follow-up was
pursued to test for bias among the non-respondents.
Insert Table 1 about here
Beginning with strong cooperation of senior managers in each source
organization, initial lists were developed of suggested names of spin-off
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entrepreneurs from that organization. Follow-up interviews were used to
screen these lists for inappropriate names as well as to generate further
suggestions in a "snowball" sample creation process. Rigorous criteria were
applied to include only those who had been former full-time employees of the
source organizations, who later participated as founders of wholly-new
for-profit companies.
Structured interviews with a detailed questionnaire, lasting typically
one to two and one-half hours, were used to gather data from each
entrepreneur personally, with telephone interviews used in less than ten
percent of the cases and mailed interviews used only as a last resort in less
than one percent of the cases. Some interviews stretched to seven or eight
hours over two or three sessions! Despite extensive efforts to include all
spin-offs from each source organization studied, no doubt some minor bias
has crept into the sample of companies studied in that it is likely that any
companies not located were less successful than those traced. The bias did
not prevent many companies from being found and studied that were clearly
failures or not very successful.
Answers to the detailed questionnaires led easily to the quantification
of information. Most all of the answers were coded and arranged in computer
data files. Incomplete information on some of the companies does not
particularly affect the data analysis as relevant codes were given to isolate
missing information.
Four clusters of influences upon a person becoming a technical
entrepreneur are discussed here: family background; education; age; and work
experience. While elements of each of these dimensions are shown to affect
the "career choice" of starting a new company, whether or not each influences
the entrepreneur's success or failure is left for later research.
BREEDING OF THE NEW ENTREPRENEURS
An understanding of the new technology-based entrepreneurs begins
most logically with an examination of their home environments or family
backgrounds. These provide the first influences that help mold the personal
development, attitudes and orientation of the future entrepreneurs. In our
studies of technical entrepreneurs, data were collected on unfortunately few
family characteristics: father's occupational status, whether or not the
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entrepreneur's father was self-employed and the entrepreneur's religious
background. Sometimes we also found out the number of brothers and sisters
in his family and the birth order among them. Wherever possible, we shall
make comparisons with the control sample (research studies of the
employees of some of the same MIT laboratories who did not spinoff to form
new companies), to place the distributions for the entrepreneurs in more
meaningful perspectives.
"The Entrepreneurial Heritage": Father's Occupational Status
The largest percentage (60%) of the technical entrepreneurs came from
families where the father was either a professional or a manager.
Comparison with the control group of MIT lab employees indicates little
difference between the two groups on this dimension. However, a closer look
at Table 2 shows four times as many entrepreneurs with professional fathers
than one might expect based on the control sample of employed scientists and
engineers. These results raise the strong possibility that sons of
professionals are more likely to become entrepreneurs than sons of managers.
(The reference to "sons" is an empirical rather a male chauvinist statement.
Only three of the 113 technical entrepreneurs in this particular group were
Insert Table 2 about here
women. Consequently, the male pronoun will be used in the remainder of this
article in referring to the entrepreneurs.) To the extent that we believe that
parents may influence their children through the example of their own
behavior, the findings are not surprising in light of the nature of the work of
the professional as opposed to that of the manager. A professional, such as a
lawyer or a physician, is seldom a member of a large hierarchical
organization. And even when in a large organization (corporation, law firm, or
hospital), the professional typically possesses a degree of independence not
held by a manager who is almost always part of a very structured
organization. After witnessing his professional father's independence, the
son is more likely to find it appealing to obtain some type of occupational
independence himself. The entrepreneurs in our studies had parents of the
previous generation, during which time period the primary occupational roles
in the United States were being served by the father. As women of the
present generation move more into both professional and managerial roles,
they will also serve increasingly as career role models for their children.
9
Whether or not the entrepreneur's father was self-employed is a second
and critical aspect related to the father's occupation. As shown inTable 3,
the difference between technical entrepreneurs and the control group of
employed scientists and engineers is quite striking. Entrepreneurs tend
strongly to come from families inwhich the father was self-employed
(X 2 =15.06, p=.0001). These findings on entrepreneurs who spun-off from MIT
labs and academic departments are strengthened by our studies of technical
entrepreneurs who originated from other source organizations.
Insert Table 3 about here
48% of the entrepreneurs who spun-off from a large electronic systems firm,
57% of those from a diversified technological corporation, 61% of the
Massachusetts biomedical entrepreneurs we studied, and 65% of a sample of
computer-related entrepreneurs had fathers who were intheir own
businesses. Although control studies are not available for direct comparison
with these sample groups, an analysis of 1960 U.S. census data (to get data
related to the entrepreneurs' fathers!) indicates that only about 25% sons of
self-employed fathers should have been expected by chance alone. Presented
statistically, the probability that a particular engineer or scientist will form
his own company is significantly greater incases where his father had his
own business (0.01). This is still further supported by our control study of
MIT faculty inwhich we found that even professorial sons of self-employed
fathers more frequently claimed a serious interest in being in business for
themselves (.05). All these evidences demonstrate that entrepreneurial
fathers produce entrepreneurial sons disproportionately, supporting the
effect of an "entrepreneurial heritage".
Yet seldom was the father's business at all related to his son's specific
entrepreneurial activities, nor were many of the father's businesses (or
occupations generally) even technical in nature. Most frequent parental
businesses were small retail stores, farms, and small non-technical
manufacturing firms. And many of the sons are in businesses that could not
have existed intheir father's time, e.g., computer software, electronic
systems or biotechnology! It is the general image and example of father as
self-employed professional or as independent business owner that provides
the role model for the son, not any specific technical or managerial
knowledge. Indeed it may be that simply familiarity with a business
10
environment, growing from "table talk" at home, is the key to increasing the
probability that an offspring will become an entrepreneur.
Religious Background
Despite expectations arising from the general literature on
entrepreneurship, no readily discernible religious differences were found
between the overall group of technical entrepreneurs and the control group of
employed scientists and engineers. A little more than half of both the
entrepreneurs and the control group were Protestant, about 25% of both
groups were Catholic, and slightly more than 20% in each group were Jewish.
At first pass it appears that simply being of a certain religious background
does not directly increase or decrease the likelihood that an individual
engineer or scientist will become a technical entrepreneur. But I'll return to
this question momentarily.
Family Size and Birth Order
The size of the family from which an individual comes also appears to
have no direct bearing on the incidence of entrepreneurship. In addition,
utilizing the family size breakdowns to establish an expected birth order, no
important differences arise between either the entrepreneurs or the control
group and the expected frequencies.
It is interesting that 55% of the entrepreneurs were first-born sons,
seemingly supporting the folkloric prediction. But when we compare them to
the control group of employed scientists and engineers, we find 54% of that
non-entrepreneur group also to be first-born sons. It is likely that much of
the general clamor about the important role of first-born sons arises from a
lack of careful statistical comparison with the family groups from which
these sons originate.
Incidentally, the entrepreneurs were born and brought up all over the
world, but with a heavy bias toward New England origins, reflecting the
sources of companies in our research samples. Of note is that ten to
twenty-five percent of each sample of new firms in our research were
founded by someone born outside of the United States. (Twelve percent of the
Swedish entrepreneurs studied by Utterback et al. had immigrated.) Technical
entrepreneurship seems to continue the "melting pot" nature of opportunities
11
for personal growth and development in the Boston area, attracting and
retaining productive talent to the region.
A Second Cut at Family Background
Relative to an individual's family background and the incidence of
technical entrepreneurship, the only significant findings concern the father's
occupational status. This is true when the entire sample is considered, that
is, the entrepreneurs and control group members with and without
self-employed fathers. As indicated earlier, the hypothesis of an
"entrepreneurial heritage" can explain why a disproportionate number of
entrepreneurs are the sons of entrepreneurs. But what explains the
entrepreneurial activity of those individuals, comprising almost half of our
samples, whose fathers were not self-employed and who therefore could not
be said to have an entrepreneurial heritage?
An examination of the family background characteristics of only those
individuals whose fathers were not self-employed reveals differences
between the entrepreneurs and the technical employees, as indicated in
Tables 4 and 5. Table 4 indicates approximately equal percentages of
entrepreneurs and control group subjects in each religious category for the
subset of the entire population whose fathers are self-employed. However,
for those whose fathers are not self-employed, Table 5 shows that the
Insert Table 4 about here
Insert Table 5 about here
Catholic group has one third fewer and the Jewish group five times more
entrepreneurs than expected based on the control group distribution (X 2=6.33,
p=.01). In the absence of the entrepreneurial heritage syndrome, religious
differences do seem to have an effect on the incidence of technical
entrepreneurship. More specifically, confirming McClelland's predictions
(1961, p. 365), more Jews and fewer Catholics can be expected to go into
business for themselves from a mixed religious population of U.S.
technologists that have no self-employed fathers.
A careful re-analysis of the data on birth order, searching for possible
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differences related to parental self-employment, produced no new insights.
To do this analysis correctly, expected frequencies of birth order
distributions needed to be calculated, based on the family size data. Much of
the literature is misleading in this regard, not accounting for family size in
the populations being studied. Our results suggest no "first-born" effect, at
least for high-technology entrepreneurs.
Thus it appears that the most influential aspect of a technical
entrepreneur's family background in his decision to become an entrepreneur is
his father's career. In the absence of a father whose career provided a role
model with a high degree of independence or autonomy, i.e., from either
self-employment or professional status, other demographic characteristics,
primarily religious background, may then have an effect on breeding
entrepreneurs.
THE ENTREPRENEUR'S EDUCATION
Many of the personal characteristics of the high-technology
entrepreneurs with whom this article is concerned are probably true of all
entrepreneurs. However, their educational characteristics provide one of the
most prominent differences.
The Collins & Moore study of Michigan entrepreneurs reported that only
about 40% of the entrepreneurs had any education beyond the high school
level. (1964) In support our somewhat comparable study of consumer
manufacturing entrepreneurs in Massachusetts had produced the same 40%
post-high school educational attainment. In contrast, however, the technical
entrepreneurs in the MIT spin-off group had a median educational level of a
Master's degree, generally in engineering. Only 1% of the total high-tech
entrepreneurs had no college education at all and only 9% did not have at least
a bachelor's degree. The distributions of educational levels for the
entrepreneurs and the control group of MIT technical employees follows in
Table 6.
Insert Table 6 about here
Probably the most important reason for these entrepreneurs' higher
educational level is the nature of the source laboratories at which they
worked prior to their enterprise formation and the training necessary for
13
employment there. Table 6 shows that the median educational level for the
control group whose members were employed at the same source labs is also
a Master's degree. The technical entrepreneurs in the two industrial spin-off
samples and in the computer-related new enterprises averaged a Bachelor's
degree plus some course work, reflecting the slightly lower educational base
of the industrial labs relative to the MIT laboratories.
A comparison of the educational levels of the technical entrepreneurs
with the general population as well as with fathers of technical
entrepreneurs (taken from the study of an electronic systems firm's spin-off
companies) follows in Table 7. The technical entrepreneurs are much better
educated than the general population, and also better educated than their
fathers, with the technical entrepreneurs heavily skewed toward the highest
levels of education. A similar median education level of a Master's degree
plus was found in our study of 29 Massachusetts biomedical entrepreneurs
and by Van de Ven et al. (1984, p. 93) in their study of 14 educational
software start-ups.
Insert Table 7 about here
Family Background and Education
A detailed analysis of a smaller subset of the MIT spin-off
entrepreneurs sought to explain further these educational levels. One finding
was that the occupational status groupings of the fathers of the
entrepreneurs (see Table 2) correlated significantly with the educational
level of the entrepreneurs (Kendall tau=0.19, p=0.06, n=58). This means that
the higher the paternal occupational status, the higher the level of education
attained by the entrepreneur. This finding was confirmed by the spin-offs of
the industrial electronics firm (0.02). No data were collected that would
permit attributing levels of income to the various occupational status levels.
However, one might expect these status levels generally to reflect
differences in income. If this is valid, then the positive correlation with
educational level can be explained in part on an economic basis. It is likely
that those entrepreneurs who came from lower occupational status families
did not have enough money to go to college as early or for as long as did those
from higher status groups. Support for this position can be derived in the
inverse statistical relationship found between paternal occupational status
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and the age of the entrepreneur when he finished the B.S. (Tau=-0.23) and M.S.
degrees (Tau=-0.30). This means that entrepreneurs who came from families
of lower occupational status (and therefore probably lower income) received
their B.S. and M.S. degrees at older ages than did entrepreneurs from higher
occupational status families (p=0.01).
The same data sample indicated a nonlinear relationship between
whether or not an entrepreneur's father was in his own business and the
education level of the entrepreneur. Table 8 shows for entrepreneurs at each
level of educational attainment the number and percent of all fathers who
were in their own businesses. A statistical test confirmed the relationship
between whether or not the entrepreneur's father was in his own business and
whether or not the educational level of the entrepreneur was at least the B.S.
degree but not more than the M.S. degree and course work (X 2 significance
level=0.07).
Insert Table 8 about here
One possible explanation for this finding is that those technical
entrepreneurs whose fathers were in their own businesses were planning to
go into business for themselves from an earlier age. Their education was
therefore targeted (consciously or unconciously) to a level appropriate to
establishing a technically based enterprise. Going beyond the B.S. or M.S.
degree was inappropriate because these sons of entrepreneurs long had in
mind the specific goal of starting a company, not of doing research, teaching,
or any other activity that might demand the still higher education of a Ph.D.
Incidentally, no correlation was found in our research studies between the
educational level of the fathers and the education of their entrepreneurial
sons.
Degree Disciplines and Sources
Search for relevant educational data in regard to degree discipline
reveals relatively little, the entrepreneurs looking more-or-less like their
technically employed counterparts. About two-thirds of the technical
entrepreneurs had degrees in engineering, thirty percent in science, and three
percent in other fields. In our separate study of biomedical entrepreneurs
more of the founders, who included several M.D.s, came from initial education
in the natural sciences.
15
Of greater interest is that of the 217 degrees earned by the 106
technical entrepreneurs in one sample we checked carefully, only three were
in management (at the Master's degree level), a similar percentage of
management degrees as found in the group of employed scientists and
engineers. In fact relatively few of the technical entrepreneurs had even
taken business courses before company formation. Of course, some of them
had co-founding partners with management education. Our more recent
research samples of entrepreneurs include an increasing percentage of
engineers with graduate management education, reflecting the growing
popularity of the MBA degree.
Influenced no doubt by the high concentration on MIT departments and
labs as sources in our research, the largest number by far of the technical
entrepreneurs we studied earned their degrees at MIT. This dominance was
also true, however, in the research data on spin-offs from non-MIT source
organizations as well as in the special industry-related new enterprise
groups, in which less bias toward MIT backgrounds might have been expected.
The Greater Boston area concentration of my research program no doubt also
explains a less-well-known phenomenon of high prevalence of entrepreneurs
trained at Northeastern University, a large urban school with the largest
"private" engineering enrollment in the country. In most of our research
samples Northeastern-educated entrepreneurs accounted for far more
companies than Harvard or other local or nationally-known educational
institutions, although a wide diversity of college backgrounds is represented
in the samples.
Education of Faculty Entrepreneurs
To this point no distinction has been made between entrepreneurs who
were MIT faculty members and those who had worked as research and
engineering staff members at MIT or elsewhere. In fact no significant
differences exist between these groups in regard to family background,
father's occupation and religion. The first major difference arises in regard
to education. Nearly all the faculty entrepreneurs had Ph.D.s, reflecting MIT's
faculty recruitment and selection criteria. For example, all nine faculty
entrepreneurs from the Mechanical Engineering department have their
doctorates (eight out of nine from MIT). Four of seven Aeronautical
Engineering faculty founders have doctorates, the other three cases being
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unique situations of age and/or circumstance. In contrast, for example, the
non-faculty Aeronautical entrepreneurs (departmental staff members)
included two doctorates and nine less-well-educated company creators.
AGE
Across all the research studies, with few exceptions, I have found
rather remarkable similarity in the age patterns of technical entrepreneurs at
the time of company founding. Table 9 shows data from eleven of my research
studies, indicating the range and median of entrepreneurial ages. The range of
ages in 243 companies is 23 to 69, with an overall median of 37 years. On an
Insert Table 9 about here
"eyeball" judgment basis, the MIT laboratory spin-offs appear to be slightly
younger, averaging 34 against a representative 38 years for their industrial
and mixed source counterparts. This may be due to more positive
encouragement at MIT for spin-off company formation as well as more access
to advanced technological bases for new firms. The 60 Swedish
entrepreneurs in the Utterback et al. (1988) sample also had a median age of
34 at the time of company formation.
To provide more detail on ages, Figure 1 shows the age distribution of
119 MIT spin-off entrepreneurs, including some from academic departments
as well as the laboratories (range = 265, median = 34). Two thirds of the
entrepreneurs started their companies when they were between the ages of
28 and 39. Only 7% of the entrepreneurs were younger than 28 and less than
10% were older than 48. Those who were older at founding had a higher
educational level (.04).
Insert Figure 1 about here
Indeed two of this MIT spin-off group were 65 years old, faculty placed
into retirement at that age who felt they still had worthwhile ideas and the
entrepreneurial energy to pursue them. The overall youth attributable to the
entrepreneurs must fundamentally be more related to attitude than
chronology alone, although this would be hard to prove.
But raising mention of the faculty does identify one of the exceptions to
17
the "rule" of mid-30s entrepreneurial ages. Examined separately, MIT faculty
form companies at significantly older ages than their staff colleagues(p=.05).
For example, in the Department of Aeronautics and Astronautics the ages of
faculty founders ranged from 26 to 65 with a median of 44 at the time of
company formation. Their non-faculty departmental colleagues who became
entrepreneurs ranged from 26 to 40 with a median of 32. The difference no
doubt reflects the longer educational periods (disproportionately more Ph.D.s,
p=.10) required by the faculty members for entry into their primary career
roles, combined with the long time then needed to secure their faculty
position. Academic tenure tends to be awarded by age 36 and few faculty
take much time away from teaching and research prior to the tenure decision.
Presence of a few faculty entrepreneurs in each of the MIT lab spin-off
groups listed in Table 8 slightly increases the median ages shown for those
samples. Without faculty entrepreneurs included the MIT lab spin-offs would
be even a bit younger in age pattern.
The second exceptional group we studied were the consumer-oriented
manufacturers, our sample of greatest contrast with the high-technology
entrepreneurs. That group ranged from 28 to 55 at the time of forming their
companies, with a median age of 45. In that regard, but no other, they look
like MIT faculty entrepreneurs. (The Collins et al. study of general
entrepreneurs in Michigan found the average age to be 52.)
Most of the technical enterprises were founded by teams of several
people, but the median ages of team members match the individual figures
shown in Table 9. In one group of 20 technical companies studied, the median
age of the youngest person on each team was 30, the median of the oldest was
41, with a median overall age of 37. A second sample of 20 companies
produced a median youngest team member age of 34, a median oldest of 43,
and a median overall founder age of 39.
Why So Young?
One probable cause of the young age of the technical entrepreneurs is
the youthful age structure of the technical organizations at which they
previously worked. A comparison of the entrepreneurial age distribution with
the employed scientists and engineers at the MIT laboratories used as control
studies shows roughly comparable age patterns, with the entrepreneurs about
two to three years younger on average. Technical organizations such as the
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MIT departments and labs, Air Force Cambridge Research Laboratory, or the
large technology-based industrial organizations in the Greater Boston Route
128 area are the breeding grounds for new enterprises. Their age base sets
the bounds within which entrepreneurship can take place. Yet on average it is
the younger people relative to these organizations as a whole who leave and
eventually start new enterprises.
A second but related influence on the young age of the technical
entrepreneurs is the newness of the technology they are using. An older man
or woman would first have to learn a new and emerging field that is the
entrepreneur's training grounds in terms both of formal education and work
experience. This can be a time-consuming and arduous task, even when
possible. A lower bound on the technical entrepreneurs' ages is set by their
almost universal college education, probably a prerequisite for acquiring the
knowledge needed to form a technical company (despite exceptional college
dropouts like Edwin Land who created Polaroid).
Depending on the particular research sample, anywhere from 75 to 100
percent of the entrepreneurs were married at the time of company
establishment, most of them with children (two on average). This suggests a
third possible influence on the youthful ages found. Financial requirements of
the family with very young children are not usually so great, at least not in
comparison with needs as the children grow and begin approaching their
college years. The burden of loss of income from giving up a well-paying
engineering job and taking less or no pay as a startup entrepreneur can be
more readily absorbed by the young family. A prospective entrepreneur in his
40s, with mid-teen aged children, is likely to be more risk averse than his
younger counterpart. Also those growing teenagers are going to be more time
demanding, competing with the heavy time requirements involved in getting a
new company off the ground.
Supporting these perspectives are the common findings of research on
job turnover. The older a person, the less likely is he or she to change jobs;
the more longevity of an individual's employment with one organization, the
less likely is job turnover; and the higher a person's position, the less likely
is job changing. Age, employer longevity and position are all highly
correlated, but each acts independently in the same direction of discouraging
voluntary departure from an organization. These general observations are
bolstered by our control studies at two major MIT laboratories: staff
19
members who are older have been at the labs longer and have higher positions
there (.001). Still older people feel more locked-in by pension considerations,
too little work time left to find a job and build a new career should the
enterprise fail, and reluctance to try something new. Risk-taking eventually
ages out!
Note that some entrepreneurs form more than one company during their
lifetime. For example, William Poduska was a co-founder of Prime Computer,
then founded Apollo Computer, and later formed Stellar Computer. The age
effects described above relate to the first-time entrepreneur. The
multi-time entrepreneur's "career" has become "forming companies", to be
continued until retirement from this career, or up to the semi-retirement
practiced by a number of ex-entrepreneurs of investment management and/or
college teaching!!
WORK EXPERIENCE
Phases of Experience
The median education level of the technical entrepreneurs (about
Master's degree, usually obtained at age of 23 or 24), coupled with their age
characteristics discussed above, leads to the logical deduction that the
typical entrepreneur had about 13 years of work experience prior to starting
his own company. Actually the mean number of years of work experience for
one group of 111 carefully-studied entrepreneurs is 12.7 years. (The variance
from this mean was almost 8 years, reflecting the inclusion in the sample of
several MIT faculty members who started their companies when they were
over 60 years of age, thus adding more than 40 years of work experience in
several cases.) This seems to mirror the average 12 years experience of the
60 Swedish technological entrepreneurs studied by Utterback et al. (1988)
and the mean of 13.2 years of experience in our biomedical sample.
Figure 2 presents the distribution of work experience, showing that the
bulk of technical entrepreneurs, 79%, had from 3 to 16 years work experience
prior to starting their new enterprises. Only 2% had less than three years and
Insert Figure 2 about here
22% had more than 16 years experience. (In contrast, the consumer
III
20
manufacturing entrepreneurs averaged 24 years of pre-enterprise work
experience.)
How did the entrepreneurs spend this 3 to16 years of
pre-entrepreneurial work experience prior to starting their new enterprises?
As was the case with educational characteristics, the nature of the work
experience of technical entrepreneurs differentiates them from other
entrepreneurs. The Collins & Moore study of Michigan entrepreneurs
characterized them as being lean on formal education but heavy on the
education one gets in "the school for entrepreneurs". (p. 125) This perceived
"school of hard knocks" was characterized by many job changes in which the
prospective entrepreneur developed the skills destined to make him effective
as an entrepreneur. Our impression is that the general entrepreneurs studied
by Collins et al. did not have one key work experience that provided the reason
and basis for a new enterprise. In contrast the technical entrepreneurs we
studied did have one key work experience which not only gave them an
opportunity to start a company that had a technological advantage at least
initially, but also, mainly through this technological edge, enabled them to
grow rapidly in sales and profits. Even in contrast with my impression of life
in California's Silicon Valley, our data did not suggest a job-jumping
phenomenon among our primarily Greater Boston entrepreneurs. In fact they
had had relatively few employers with one, namely what I have been calling
"the technology source organization", being the most important.
The entrepreneur's total work experience can be broken down into three
segments: his work if any prior to "the technology source organization", his
work at that organization, and his work if any between the "sources" we
studied and the new enterprise. The mean number of years work experience
prior to working for the source laboratory was 4.26 years (with variance
about this mean of 5.59 years). More strikingly, for 41% of the entrepreneurs
employment at the source organization was their first job. Fully 70% of the
entrepreneurs had had 5 or less years experience elsewhere when they went
to work for one of these source laboratories.
What about the segment of the entrepreneur's work experience between
the technology source organization and the founding of the new enterprise?
Depending on the research sample chosen within our studies, between fifty
and sixty percent went directly into their new businesses after they
terminated employment at the source laboratory we assessed. About eighty
21
to eighty-five percent had 5 or less years work experience between the
laboratory and the new enterprise. (Incidentally, I have also found that the
time lag between the source laboratory and the new enterprise had a strong
effect on the degree to which technology was transferred. Namely, the longer
the time lag the less the degree of technology transfer.) The average number
of years between the laboratory and the new enterprise was 2.4 years. Figure
3 indicates the evidence of new company formation after departure from the
source organizations.
Insert Figure 3 about here
Where then do our technical entrepreneurs get their principal training,
experience and skills? For some it came from their experience prior to the
source laboratory and for some, but many less, it came from their work
between the so-called "source" and the new enterprises. But the vast
majority of the technical entrepreneurs studied gained their relevant
experience and training from one key period of work experience, that at the
technology source organization. The typical entrepreneur spent 7.4 years at
the so-called source organization (again biased upward by the inclusion of
faculty entrepreneurs), longer by almost 3 and 5 years respectively than the
average years worked before the laboratory and between the laboratory and
the new enterprise. Fifty-three percent of the entrepreneurs spent 5 or more
years at the source laboratory.
Almost 21% of the total sample of entrepreneurs worked only at what I
have called a technology source laboratory. This is true not only for MIT
spin-offs, but, for example, for those new enterprises formed out of the
electronic systems company we studied. Another 37% worked only before and
at the lab, which makes up the total here of 58% who went directly from the
"source" organization to the new enterprise. Twenty percent worked at the
source and between it and the new enterprise. Only approximately 22% of the
entrepreneurs worked before, at, and after the source laboratory before
starting their new enterprises. These data are summarized in Table 10.
Insert Table 10 about here
Along with the notion that technical entrepreneurs usually have one key
work experience, on the basis of which they start their new enterprises, must
necessarily come some consideration of the elements of experience at that
III
22
organization. We measured four factors to characterize the entrepreneurs'
experience at the source organizations studied: number of papers published
and patents granted while at the "source"; percentage of time spent
performing various types of work activities; kind of technical work engaged
in on a scale from basic research to development; and lastly, several
attitudes as to what the entrepreneur gained in terms of challenge and
personal satisfaction from his work.
Productivity
Wherever possible I will consider the characteristics of the
entrepreneurs' work experience in relation to those of the control group of
employed scientists and engineers at those same source organizations. Note
that the typical entrepreneur had spent 7.4 years at the technology source
organization, while the typical non-entrepreneur staff member of these same
technology source organizations had already been there 8.4 years when we
collected our data, one full year longer than the entrepreneurs. (Obviously,
the typical non-entrepreneur will be there even longer before eventually
departing the source organization!) As a result when factors are considered
such as papers published or patents granted during employment at the source,
there is a bias of more years during which, for example, the non-entrepreneur
staff member might have published more papers.
Despite this bias Table 11 indicates that the entrepreneurs while
employed by the technology source organizations published almost three
times as many papers per person as did the control group employees. In
addition, almost twice as many entrepreneur employees as non-entrepreneurs
published at least one paper while employed by these labs.
Insert Table 11 about here
Examination of patents granted while at the source organizations
reveals the same type of phenomenon. During employment by the technology
source organizations, the typical entrepreneur was granted 32 times as
many patents (a clearer index of commercial entrepreneurial tendencies than
papers!) as his non-entrepreneur counterpart. In addition, 34% of the
entrepreneurs were granted at least one patent while at these laboratories as
opposed to only 5% of the control group. These data demonstrate a striking
difference, even on measures of conventional technical productivity, between
23
the work experiences of the entrepreneurs and the control group. Even within
the control group of employed scientists and engineers, those few who had
been previously self-employed at one time or another had been granted far
more patents prior to their employment by the source organization than their
colleagues who had never been self-employed (.001). Apparently, not only do
entrepreneurs start companies but they are among the most productive
technical contributors while employed in research and development
organizations.
Time Allocation
Another aspect of the entrepreneur's work experience at the technology
source organization is the percentage of his time spent on various activities
such as report writing, development, and so forth. Our data show that on the
average both the entrepreneurs and the control group spent about 50% of their
time doing direct research and development work. When we consider only the
mean percentages of time spent at the various activities, the entrepreneurs
appear to be much like the control group. However, the differences in the
standard deviations about the means for the entrepreneurs and the control
group indicate that on each activity the entrepreneurial group has much higher
degree of variation. These data present a picture of individual entrepreneurs
as working on particular activities for very different amounts of time, as
opposed to the control group where percentages of time spent on the same
activities do not differ much between individuals or from the average.
Entrepreneurs who had a longer employment at the industrial
electronics laboratory studied not unexpectedly spent a significantly greater
amount of time on personnel supervision (.01), confirming an earlier MIT
research study of R&D management at that same source organization. (Rubin,
Stedry & Willits, 1965) Though exact job position was not uniformly
recorded in the studies, it appears that many of the entrepreneurs were
technical supervisors in their source organizations. For example, 40% of the
Instrumentation Lab entrepreneurs were technical supervisors at the Lab
before founding their own ventures. By contrast, only about 15% of the Lab
staff members are supervisors. Several factors contribute likely
explanations for the higher entrepreneurial defection of supervisors: (1)
their personal characteristics that led them into supervisory roles; (2) their
exposure to a wide variety of problems; and (3) the high degree of
responsibility they typically are given as supervisors. They come into
24
contact with suppliers and customers much more often than the average
engineer, giving them a valuable source of market information and contacts.
Nature of Work
Beyond this simple inquiry into time allocation we made a more serious
attempt to get each entrepreneur (as well as each employed scientist and
engineer in our control studies) to characterize the technical nature of the
work he performed. The approach for doing this was borrowed from a rather
classic U.S. Department of Defense (DOD) comprehensive study of sources of
technical advances that were embodied in the Bullpup missile project, this
study in turn being part of Project Hindsight, a detailed analysis of military
R&D productivity. (Office of the Director of Defense Research and Engineering,
1965) As such the nine classes of R&D work which follow, widely tested for
reliability by the DOD, will be referred to as the individual's "Bullpup
Classification":
1. Investigations in pure and applied mathematics and theoretical
studies concerning natural phenomena.
2. Experimental validation of theory and accumulation of data
concerning natural phenomena.
3. Combined theoretical and experimental studies of new or unexplored
fields of natural phenomena.
4. Conception and/or demonstration of the capability of performing a
specific and elementary function, using new or untried concepts, principles,
techniques, materials, etc.
5. Theoretical analysis and/or experimental measurement of the
characteristics of behavior of materials, equipment, etc., as required for
design.
6. Development of a new material necessary for the performance of a
function.
7. First demonstration of the capability of performing a specific and
elementary function, using established concepts, principles, materials, etc.
25
8. Development of a new manufacturing, fabrication and materials
processing technique.
9. First development of a complete system, component, equipment or
major element of such equipment, using established concepts, principles,
materials, etc. -- Prototype development.
Suffice it to say that 1 is basic research, 9 is prototype development,
and in ascending from 1 to 9 the classifications become more developmental
in orientation, as follows:
1
2
3
4
5
6
7
8
9
Basic
Prototype
Research --- Increasingly Developmental--- Development
In some cases the assignment of classification by the coder was
difficult due to the variety of work performed by the entrepreneur at the
source technical organization. However, the more highly educated staff
members are heavily skewed to the basic research end of the scale at Lincoln
Laboratory (.001) and two other MIT labs (.08, .15) and among the electronic
systems company spin-offs (.02). The assigned classification does correlate
positively with the entrepreneurs' reporting of the percent of time spent on
development work at MIT's Research Laboratory for Electronics (RLE) (.09) and
at the MIT Electronic Systems Lab (ESL) (.05); and it correlates negatively
with the entrepreneurs' reporting of the percent of time spent on research at
RLE (.007), at ESL (.01) and among the electronic systems company spin-offs
(.003). In addition these "Bullpup" classifications correlate with those
assigned by the individuals' supervisors at two laboratories (.007, .13).
These findings support adopting the "Bullpup" classification as a usable
measure of the nature of work performed by the entrepreneur while at the
source organization.
Figure 4 pictures the distribution of entrepreneurs' technical work at
the electronic systems company from which we traced spin-off enterprises.
Insert Figure 4 about here
III
26
While at least one founder was classified in each "Bullpup" category the
distribution is skewed heavily toward the developmental end of the spectrum,
as might be expected in an industrial systems-oriented organization.
In Table 12 a subset of ninety-four MIT-based entrepreneurs are
categorized by the source organization from which they spun-off and by the
type of work they performed at the source. The totals indicate that only two
Insert Table 12 about here
of the entrepreneurs performed work that was primarily basic research
while twenty-six did primarily prototype development work. This is
understandable. Basic research does not present much technology that has
immediate practical utility. It is not supposed to. Prototype development
work on the other hand involves much technology of practical utility. That is
its nature. Entrepreneurs should be expected to come to a greater degree
from the more developmental types of work.
The table also provides further support for the credibility of the coding
scheme we used to categorize the entrepreneurs' prior work. The
entrepreneurs from MIT academic departments, who included many faculty,
did much more research-oriented work than those who originated from the
major MIT laboratories (0.01).
Table 13 shows the work ratings of randomly sampled MIT Lincoln
Laboratory personnel and Instrumentation Laboratory personnel (from our
control studies) along with their related spin-off entrepreneurs. In each
comparison the entrepreneurs were more developmentally oriented than their
laboratory counterparts (.06, .13). A subjective evaluation of the
entrepreneurs' work done at two other MIT laboratories, the Electronic
Systems Lab and the Research Laboratory for Electronics, drew the same
conclusion. We can now conclude that the more developmentally oriented
people do have a greater tendency to become entrepreneurs.
Insert Table 13 about here
Attitudes Toward Source Organization
27
An individual's attitude toward his work plays an important role in his
learning process. A person who finds his work challenging and enjoyable can
be expected to learn more easily and develop his skills more fully. With few
exceptions the interviewed entrepreneurs spoke very highly of their "source"
technical organizations. For example, the MIT spin-offs typically said that
their MIT lab work had been the most interesting work they had done, often at
the leading edge or frontiers of science and technology. Eighty-nine percent
indicated that their lab was a place in which they had learned substantial new
technology, in contrast to primarily applying knowledge they had already
possessed.
84% of the MIT-based entrepreneurs indicated that their work at their
"source" laboratory or department organization was above average in
challenge. Sixty-five percent indicated furthermore that their "source" work
was at least as or more challenging than any other work experience they had
before starting their new enterprises. Ninety-two percent of the
entrepreneurs indicated that they had derived above average enjoyment and
personal satisfaction from their work at the source organization.
Seventy-six percent felt that their laboratory experience was at least as or
more enjoyable than any other employment prior to starting their new
enterprises. Not surprisingly the challenge of the work and the satisfaction
and enjoyment derived from doing the work are strongly related (Kendall
Tau=.60, p=.001). This relation between prior work challenge and satisfaction
was true even for the consumer manufacturing entrepreneurs (.04).
The entrepreneurs who had come from the electronics systems company
had similar attitudes. Only two of those individuals disliked the industrial
laboratory and only one felt that it offered no challenge. The entrepreneurs
on the whole felt the challenge about equal to that of other companies they
had worked for and said that they enjoyed the work at the source laboratory
more than that at other laboratories. It was obvious that the technical
entrepreneur has a strong dislike for meetings. Those entrepreneurs who
found the work most challenging tended to spend the least time in meetings
while at the laboratory (.04). Similarly, the entrepreneurs who most enjoyed
their work at the company spent very little time in meetings (.008), those
entrepreneurs also generally preferring development work to all other work,
as indicated earlier.
Occasionally an entrepreneur reported leaving the source organization
III
28
with unpleasant feelings. For example, out of 23 Instrumentation Lab
spin-offs providing this information two felt their managerial talents were
being underutilized (perhaps intentionally so, given Lab policies to encourage
turnover), and a third entrepreneur had been asked to leave. But negative
attitudes were uncommon.
Indeed, many of the entrepreneurs indicated that they had become so
involved with their work on a given project at the source organization that
when these projects were completed they felt that their work too was
completed. Several of the entrepreneurs attested that their sense of
identification with the source lab began to wane as the project neared
completion. Only through the challenge of starting their own enterprises did
they think they could recapture the feelings that they were doing something
important. The information required to do a rigorous analysis of the effect of
completion of projects on new company startups was not available, however.
Psychological aspects of the entrepreneurs' motivations are presented
elsewhere. (Roberts, 1988)
SUMMARY
This article sought to explain empirically the origins of the
technology-based entrepreneur, comparing several samples of spinoff
entrepreneurs with appropriate control groups of employed scientists and
engineers. The major conclusions are presented here and synopsized in Table
14. The first and perhaps most important finding is what I have labelled "the
entrepreneurial heritage": a strong tendency for entrepreneurs to come from
families in which the father was self-employed. This phenomenon
characterizes 50 to 65 percent of the technical entrepreneurs, depending on
specific sub-sample, and is at least twice what should be expected from a
purely random sample of the U.S. population.
Secondly, for those who do not come from entrepreneurial homes,
religious differences do affect the incidence of technical entrepreneurship.
In line with McClelland's writings about achievement motivation being linked
to certain religious and ethnic family backgrounds, relatively more Jews and
fewer Catholics establish technology-oriented firms.
But careful analyses dispel the myth of the first-born son as being
significantly related to technical entrepreneurship. Proportionately as many
29
first-borns are likely to remain as employed scientists and engineers as are
likely to spin-off to their own firms.
In contrast with prior studies of "general" entrepreneurs, our research
demonstrates the technical entrepreneur to be well-educated, the median
educational level being from slightly below to slightly above a Master's
degree, more typically in engineering than in science, and only infrequently
educated in all other disciplines including management.
The mid-30s is the dominant age range of technical founders, with MIT
laboratory spin-offs being on the younger side and faculty entrepreneurs
being older exceptions to the age "rule" in our several sub-samples of
entrepreneurs.
The typical technical entrepreneur had 13 years of work experience,
more than half of it at the incubator technical organizations I have labelled
the "source" organizations. Close to two-thirds of the entrepreneurs went
directly from these dominant technical work experience sites into their own
companies. At the source organizations the entrepreneurs significantly
outproduced their technical colleagues along the conventional output
measures of papers and patents. Many had already risen into technical
supervisory roles. Indeed, starting a company might be just another avenue
for the productive energies and knowledge of these outstanding people!
Their work backgrounds evidence that not only are entrepreneurs more
likely to come from engineering rather than science, but especially from the
developmental (not research) end of the R&D work spectrum. Translating
technology into use is more likely to spawn entrepreneurs than is the more
basic creation of new technical knowledge.
Insert Table 14 about here
III
30
REFERENCES
Atkinson, J. W. 1958. Motives in fantasy. action and society. Princeton, New
Jersey: D. Van Nostrand Co.
Collins, O. F., Moore, D. G., with Unwalla, D. B. 1964. The enterprising man.
East Lansing, Michigan: Michigan State University Press.
Cooper, A. C. 1971. Spin-offs and technical entrepreneurship. IEEE
Transactions on Engineering Management, EM-18(1): 2-6.
Cooper, A. C. & Bruno, A. V. 1977. Success among high technology firms.
Business Horizons, 20(2): 16-22.
Hagen, E. E. 1963. On the theory of social change. Homewood, Illinois: Dorsey
Press.
McClelland, D. C. 1961. The achieving society. Princeton, New Jersey: D. Van
Nostrand Co.
Office of the Director of Defense Research and Engineering. 1964. A trial
study of the research and exploratory-development origins of a
weapon system. .. Bullpup. Washington, D.C.: 26-27.
Roberts, E. B. 1968. Entrepreneurship and technology: A basic study of
innovators. Research Management, 11(4): 249-266.
Roberts, E. B. 1988. The personality and motivations of technological
entrepreneurs. Working Paper #2078-88, MIT Sloan School of Management.
Cambridge, Massachusetts.
Rubin, . M., Stedry, A. C., & Willits, R. D. 1965. Influences related to time
allocation of R&D supervisors. IEEE Transactions on Engineering
Management, EM-12(3).
Sexton, D. L. & Smilor, R. W. 1986. The art and science of entrepreneurship.
Cambridge, Massachusetts: Ballinger Publishing.
Tyebjee, T. T. & Bruno, A. V. 1982. A comparative analysis of California
31
startups from 1978 to 1980. In K. H. Vesper (Ed.), Frontiers of
entrepreneurship research: 163-176. Wellesley, Massachusetts: Babson
College.
Utterback, J. M., Meyer, M., Roberts, E. & Reitberger, G. 1988. Technology and
industrial innovation in Sweden: A study of technology-based firms
formed between 1965 and 1980. Research Policy, 17: 15-26.
Van de Ven, A.H., Hudson, R. & Schroeder, D. M. 1984. Designing new business
startups: Entrepreneurial, organizational, and ecological considerations.
Journal of Management,10: 87-107.
Weber, M. 1956. The Protestant ethic and the spirit of capitalism. New York:
Scribner.
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TABLE 1
Data Sources for Entrepreneurial Backgrounds Study
A. Basic Information on Entrepreneurial Spin-offs from Technological
Source Organizations
New Companies Participants in
Research Study
Identified
Sources of New Enterprises
MIT major laboratories (4 studies)
MIT academic departments (5 studies)
Air Force Cambridge Research Laboratory
MITRE Corporation
Electronic systems company
Diversified technological company
Totals
107
74
16
5
45
58
305
96
60
15
5
39
23
238
B. Comparative Study of Non-Technical New Enterprises
Consumer-oriented manufacturers
51
12
C. Control Studies of Employed Scientists and Engineers
MIT major laboratories (2 studies)
391 persons
299 persons
* Among my research assistants and thesis students who contributed
importantly to this research were Erich K. Bender, Frederick L. Buddenhagen,
Howard A. Cohen, Dean A. Forseth, Jerome Goldstein, Michael W. Klahr, Donald
H. Peters, John C. Ruth, Christopher L. Taylor, and Paul V. Teplitz, as well as
my former research associate Herbert A. Wainer.
33
TABLE 2
Father's Occupational Status
Father's Occupational
Status
Professional
Managerial
Clerical & Sales
Skilled Labor
Unskilled Labor
Farmer
Totals
Technical
Entrepreneurs
(n=113)*
%/
32
27
9
18
10
4
100%
Employed Scientists and
Engineers (S&E)(Control Group)
(n=296)
%
8
44
7
21
11
8
99%+
* Data on 113 entrepreneurs were used for the comparison shown here. Total
sample sizes vary from possible maximum throughout this article due to
missing data and the use of various samples.
+ Round-off error
III
34
TABLE 3
Whether or Not Father was Self-Employed
Father Self-Emploved
Yes
No
Totals
X2=15.06, p=0.0001
Technical
Entrepreneurs
(n=119)
%/
Employed Scientists
and Engineers
(n=296)
51
49
30
70
100
100
35
TABLE 4
Religion of Those with Self-Employed Fathers
Technical
Entrepreneurs
(n=51)
Religious
Background
Protestant
Catholic
Jewish
41
40
27
25
31
Totals
*Round-off error
Employed
Scientists & Engineers
(n=83)
99%*
35
100%
III
36
TABLE 5
Religion of Those with Fathers who were Not Self-Employed
Technical
Entrepreneurs
(n=50)
Religious
Background
Employed
Scientists & Engineers
(n=1 68)
%/
Protestant
Catholic
Jewish
Totals
X 2 =6.33, p=0.01
70
20
10
100%
68
30
2
100%
37
TABLE 6
Educational Level
Technical
Entrepreneurs
(n=1 24)
Educational
Level
High school
College without degree
B.S.
B.S. plus courses
M.S.
M.S. plus courses
Professional Engrg. degree
Ph.D.
Totals
*Round-off error
Employed
Scientists & Engineers
(n=299)
Cumulative
1
8
7
20
18
11
3
31
1
9
16
36
54
65
68
99*
99%*
Cumulative
1
5
8
31
12
21
2
20
1
6
14
45
57
78
80
100
100%
III
38
TABLE 7
Educational Distribution of Technical Entrepreneurs
Compared to Other Groups (%)
Educational
Level
Less than High
School
Some High School
High School
Graduate
Some College
College Graduate
General
Population
Fathers of
Technical
Entrepreneurs
Technical
Entrepreneurs
23
0
0
15
5
53
1
5
25
90
58
15
9
39
TABLE 8
Entrepreneurs within Each Educational Level
Whose Fathers were Self-Employed (n=96)
Educational
Level
No school beyond
high school
Entrepreneurs
in each
Educational
Group
(Number)
Fathers
Were in Own
Business
(Number)
Total
(Percent)
1
0
0
Percentile
Combination
of Paired Groups
(Percent)
33
College without any
degree
8
3
38
B.S. degree
2
2
100
56
B.S. degree and
course work
16
8
50
M.S. degree
13
8
62
61
M.S. degree and
course work
10
6
60
Professional
engineering degree
3
1
33
36
Ph.D. or greater
Totals
11
4
36
64
32
50
III
40
TABLE 9
Age Distribution of Technical Entrepreneurs
Sample Size
Age Range
Median
MIT Laboratory Spin-off Studies
Electronic Systems Laboratory
Instrumentation Laboratory
Lincoln Laboratory
Research Laboratory for Electronics
11
27
47
13
27-43
24-55
25-65
29-64
35
33
34
36
23
42
25-54
24-51
39
37
18
20
26-52
248
39
37
21
243
35
20
269
39
Other New Enterprises Research Studies
Spinoffs from diversified
technological company
Computer-related firms (2 studies)
Recently formed high-technology
firms
Analyses of business plans
Search processes for raising
venture capital
In-depth analyses of venture
capital investment decisions
41
TABLE 10
Work Experience before Starting the New Enterprise
Work Experience
Technical
Entrepreneurs (%)
Technology source organization only
20.6
Technology source organization plus
employment between it and the new
enterprise
19.6
Experience before the source organization
and at it
37.4
Experience before, at and after the
technology source organization
22.4
III
42
TABLE 11
Papers and Patents--Entrepreneurs vs. Control Group
Technical
Entrepreneurs
Employed Scientists
and Engineers
Control Group
Papers published per person
while at the source laboratories
6.35
2.2
Percent who published at least
one paper while at the labs
63%
38%
Patents granted per person
while at the source labs
1.6
.05
Percent who were granted at least
one patent while at the labs
34%
5%
43
TABLE 12
Nature of Work of MIT Spin-off Entrepreneurs (n=94)
1
Work Classification ("Bullpup" scale)
2
.
4
7
8. 9
Totals
Major Laboratories
Electronic Systems Lab
Instrumentation Lab
Lincoln Lab
Research Laboratory
for Electronics
-
1
2
4
10
3
1 7
1 12
9
32
4
-
2
12
8
4 25
63
--
1-
6
4
3
2
1
3
8
5
10
1
3
-
2
4
8
-
5
2
-
1
-
1
1
1
9
8
8
1
17
13
18
2
Labs Totals
Median=7, Mean=6.6
- 3-
2
1
1
Academic Departments
Aeronautics and
Astronautics
Electrical Engineering
Materials Science
Depts. Totals
-
..1
17
6
1
1
-
8
1
2
1
1
31
2
10
5 26
94
Median = 4, Mean = 4.5
2
MIT Totals
11__------
_
III
44
TABLE 13
Nature of Work of Source Personnel and Spin-off Entrepreneurs
Sample
1
Work Classification ("Bullpup" scale)
7 8 9
2
3 4
5 fi
Lincoln
Laboratory
Personnel*
9
15
Lincoln
Spin-Off
Entrepreneurs
2
Instrumentation
Personnel**
3
Instrumentation
Spin-Offs
3
22
16
6
4
2
16 4
1
56
5
5.75 150
1
3 1
12
6
6.06
18 14 1
6 6
81
9
7.33 134
7
9
8.44
3
1
-
11
Median Mean Total
-
1
* The difference in the work ratings of the Lincoln personnel and the Lincoln
spin-offs is statistically significant at the probability level of .06
(Mann-Whitney U Test).
** Significant at a level of .13 (Mann-Whitney U Test).
32
9
45
TABLE 14
Characteristic Influences on Becoming a Technical Entrepreneur
Family Background
"Entrepreneurial heritage" -- son of self-employed father
Some influenced by achievement-oriented religious background
Age and Education
Master's degree, usually in engineering
Mid-30s age at founding
Work Experience
Decade plus of work, dominated by experience in "source organization"
Developmental (rather than research) work orientation
Highly productive technologist
111
46
FIGURE 1
Age Distribution of MIT Spin-off Entrepreneurs
at the Time they Started their New Enterprises (n=1 19)*
%
0
f
E
n
t
r
e
p
r
e
n
e
u
r
s
- o
.1
18
16
14
12
10
8
6
---..
.....
....
:....
4
2
.::
..
I.
....I '
0 __E17.:7':1
._..__..__
-
I
22-24
I
. .-.
....
:::::
.. :
*1w
I
._
I
28-30
.. .
r
I
34-36
_
L
I
.. ..
I
40-42
_
I
.....
::::
: ....... ::: i
*,-,l-,-. :::: _:: .- t..
I
46-48
I
I
52-54
Entrepreneurs' Age at Time of Founding
Round-off error; adds to 102 %
r.-,-
Imr.-.1
I
:::
I
58-60
I
64-66
47
FIGURE 2
Work Experience of Technical Entrepreneurs
Prior to Starting their New Enterprises (n=1 11)*
20
0
f
E
n
t
r
e
p
r
e
n
e
U
r
S
18
m
16
::
:::
14
I:
::
12
:::
.:
10
8
-
I
:.
6
:::
.:
..
.
:::
.:
4
_!
:..
:::
2
:.'.
.....
0
~
-
---
I
1-2
r.
-
'1
3
I
'
I
5-6
iC
3
I
ili;r
I
u
I
9-10
I
I
13-14
ii-
::.'
:::
I(:
I:f··l
L'.:
:.'
::
·
I
I
I
·:
:
:::
17-18
.
21-22
:::
::
:::
').'
.
I
25-26
Number of Years Work Experience
Round-off error; adds to 103 %
.
I
.
I
29-30
.
I
.
I
33-34
I
.
I
.
37-38
f
I
48
FIGURE 3
New Enterprise Formation
Related to the Years after Termination of Employment at the Source (n=1 21)
70
60
50
Number
of
Companies
40I
--
30
2C
10
I-
u
0
2
4
6
8
10
12
14
16
Number of Years between Leaving
Source and Formation of New Enterprise
49
FIGURE 4
Nature of Entrepreneurs' Laboratory Work
in Electronic Systems Company (n=35)
N
U
10
m
b
e
r
8
0o
6
f
E
n
t
r
e
p
r
e
n
e
u
r
s
9
7
5
4
3
2
1
0
_I
r
Research"
III
1II
I
I
·II
·II
·
I
1I
II
Development"
"Bullpup" Classification